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A Mirror Descent Perspective of Smoothed Sign Descent
Shuyang Wang, Diego Klabjan
Recent work by Woodworth et al. (2020) shows that the optimization dynamics of gradient descent for overparameterized problems can be viewed as low-dimensional dual dynamics induce…
Scalable Multi-agent Reinforcement Learning for Factory-wide Dynamic Scheduling
Jaeyeon Jang, Diego Klabjan, Han Liu +5
Real-time dynamic scheduling is a crucial but notoriously challenging task in modern manufacturing processes due to its high decision complexity. Recently, reinforcement learning (…
Communication-Efficient Federated Low-Rank Update Algorithm and its Connection to Implicit Regularization
Haemin Park, Diego Klabjan
Federated Learning (FL) faces significant challenges related to communication efficiency and performance reduction when scaling to many clients. To address these issues, we explore…
Rewind-to-Delete: Certified Machine Unlearning for Nonconvex Functions
Siqiao Mu, Diego Klabjan
Machine unlearning algorithms aim to efficiently remove data from a model without retraining it from scratch, in order to remove corrupted or outdated data or respect a user's ``ri…
Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network
Jaeyeon Jang, Diego Klabjan, Veena Mendiratta +1
Federated learning is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server. Most exis…